农业工程学报Issue(8)：187-194,8.DOI:10.3969/j.issn.1002-6819.2013.08.022
基于有限脉冲反应和径向基神经网络的触电信号识别
Recognition of electric shock signal based on FIR filtering and RBF neural networks
摘要
Abstract
Residual current protection device (RCD) has been widely used in low-voltage, rural power grids because it plays a very important role in avoiding physical shock, equipment damage, and electrical fires, etc, caused by leakage. At present, a setting value of leakage current can often be used as a key action for RCD. However, the electric shock current signal of the human body cannot be detected, and when unexpected current values close to or more than the setting value emerge, this will lead to the malfunction of RCD. In order to overcome the shortcomings above, we present a new recognition method for electric shock signal using digital filter technology and radial basis neural network. The method has three main stages. First, total leakage current and electric short current has been pre-processed using the finite impulse response digital filtering, which was designed by choosing suitable window functions and filter order. Second, the pre-processed signals are trained to create a three-level radial basis neural network. Last, the electric short current can be recognized by inputting the filtered total leakage current signal to the radial basis neural network, thus establishing the detection model. Experiments showed that the proposed method achieves an average relative error of 3.76% between detected value and actual value. The robustness, adaptability, and practicality of the proposed method also have been proven by the results. The proposed method made a definite practical significance for developing a new device of residual current protection.关键词
农村地区/泄漏电流/神经网络/FIR数字滤波器/窗函数/径向基函数/触电信号识别Key words
rural areas/leakage currents/neural networks/FIR digital filter/window function radial basis function/electric shock signal detection分类
信息技术与安全科学引用本文复制引用
关海鸥,杜松怀,李春兰,苏娟,梁英,武子超,邵利敏..基于有限脉冲反应和径向基神经网络的触电信号识别[J].农业工程学报,2013,(8):187-194,8.基金项目
国家自然科学基金(51177165)、中国农业大学博士创新基金资助项目(2012YJ112)和国家电网公司科技项目(PD17201200033) （51177165）
